Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science).

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science). Bioinform Biol Insights. 2018;12:1177932218759292 Authors: Zeng ISL, Lumley T Abstract Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fe...
Source: Bioinformatics and Biology Insights - Category: Bioinformatics Authors: Tags: Bioinform Biol Insights Source Type: research